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In [1]:
import pandas as pd
import urllib
import numpy as np
import urllib.request
import re
from textblob import TextBlob
%run lib.py
In [2]:
#name="Legally%20Blonde"
#name="aboutmary"
#name="10Things"
name="magnolia"
#name="Friday%20The%2013th"
#name="Ghost%20Ship"
#name="Juno"
#name="Reservoir+Dogs"
#name="shawshank"
#name="Sixth%20Sense,%20The"
#name="sunset_bld_3_21_49"
#name="Titanic"
#name="toy_story"
#name="trainspotting"
#name="transformers"
#name="the-truman-show_shooting"
#name="batman_production"
In [3]:
ext="html"
txtfiles=["Ghost%20Ship", "Legally%20Blonde", "Friday%20The%2013th", "Juno", "Reservoir+Dogs", "Sixth%20Sense,%20The", "Titanic"]
if name in txtfiles:
    ext="txt"
fp = urllib.request.urlopen("http://www.dailyscript.com/scripts/"+name+"."+ext)
mybytes = fp.read()

mystr = mybytes.decode("utf8", "ignore")
fp.close()
liston=mystr.split("\n")
liston=[s.replace('\r', '') for s in liston]
liston=[re.sub('<[^<]+?>', '', text) for text in liston]
In [4]:
if name=="shawshank":
    liston=[i.replace("\t", "    ") for i in liston]
In [5]:
char=""
script=[]
charintro='                                 '
endofdialogue='          '
dialoguepre='                    '
newscenepre='          '
charintro=''
endofdialogue=''
dialoguepre=''
newscenepre=''
i=45
print("Characters")
i, charintro=nextbigchunk(liston, i)
print("Adverbs")
i, adverb=nextbigchunk(liston, i, adverbs=True)
print("Dialogues")
i, dialoguepre=nextbigchunk(liston, i)
print("New Scene:")
i, newscenepre=nextbigchunk(liston, i)

if newscenepre=="X":
    i=100
    i, newscenepre=nextbigchunk(liston, i)
    if name=="aboutmary":
        newscenepre=" ".join(["" for i in range(56)])
    if len(newscenepre)==len(charintro):
        newscenepre="X"
    

endofdialogue=newscenepre
    

scene=1
for s in liston:
    if s[0:len(charintro)]==charintro and s[len(charintro)]!=" " and s.strip()[0]!="(" and s.strip()[len(s.strip())-1]!=")":
        #print("Charatcer*****")
        char=s[len(charintro):]
        new=dict()
        new['char']=char.strip()
        new['dialogue']=""
        new['scene']=scene
        new['adverb']=""
    if s==endofdialogue or s.replace(" ", "")=="":
        if char!="":
            char=""
            script.append(new)
    if char!="" and s[0:len(dialoguepre)]==dialoguepre and s[len(dialoguepre)]!=" ":
        #print("Dialogue******")
        if new['dialogue']!="":
            new['dialogue']=new['dialogue']+" "
        new['dialogue']=new['dialogue']+s[len(dialoguepre):]
    if char!="" and ((s[0:len(adverb)]==adverb and s[len(adverb)]!=" ") or (len(s)>1 and s.strip()[0]=="(" and s.strip()[len(s.strip())-1]==")" )):
        if new['adverb']!="":
            new['adverb']=new['adverb']+" "
        new['adverb']=new['adverb']+s[len(adverb):]
    if s[0:len(newscenepre)]==newscenepre and len(s)>len(newscenepre) and ( s.isupper()) and s[len(newscenepre)]!=" ":
        scene=scene+1
Characters
                                magnolia
                                NARRATOR
                                NARRATOR
                                NARRATOR
                                NARRATOR
                                NARRATOR
Adverbs
Dialogues
                      In the New York Herald, November 26,
                      year 1911, there is an account of the
                      hanging of three men --
                      ...they died for the murder of
                      Sir Edmund William Godfrey --
                      -- Husband, Father, Pharmacist and all
New Scene:
     a P.T. Anderson picture                             11/10/98
     a Joanne Sellar/Ghoulardi Film Company production
     
     
     
     
In [6]:
pd.DataFrame(script).to_csv(name+'.csv', index=None)
pd.DataFrame(script)
Out[6]:
adverb char dialogue scene
0 magnolia 1
1 NARRATOR In the New York Herald, November 26, year 1911... 2
2 NARRATOR ...they died for the murder of Sir Edmund Will... 2
3 NARRATOR -- Husband, Father, Pharmacist and all around ... 2
4 NARRATOR Greenberry Hill, London. Population as listed. 3
5 NARRATOR He was murdered by three vagrants whose motive... 5
6 NARRATOR ...Joseph Green..... 5
7 NARRATOR ...Stanley Berry.... 5
8 NARRATOR ...and Nigel Hill... 5
9 NARRATOR Green, Berry and Hill. 7
10 NARRATOR ...And I Would Like To Think This Was Only A M... 7
11 NARRATOR As reported in the Reno Gazzette, June of 1983... 9
12 NARRATOR --- the water that it took to contain the fire -- 10
13 NARRATOR -- and a scuba diver named Delmer Darion. 12
14 NARRATOR Employee of the Peppermill Hotel and Casino, R... 15
15 NARRATOR -- well liked and well regarded as a physical,... 16
16 NARRATOR -- as reported by the coroner, Delmer died of ... 21
17 NARRATOR ...volunteer firefighter, estranged father of ... 24
18 NARRATOR -- added to this, Mr. Hansen's tortured life m... 26
19 CRAIG HANSEN ...oh God...fuck...I'm sorry...I'm sorry... 27
20 NARRATOR The weight of the guilt and the measure of coi... 27
21 CRAIG HANSEN ...forgive me... 27
22 NARRATOR And I Am Trying To Think This Was All Only A M... 29
23 NARRATOR The tale told at a 1961 awards dinner for the ... 32
24 NARRATOR Seventeen year old Sydney Barringer. In the ci... 33
25 NARRATOR The coroner ruled that the unsuccessful suicid... 33
26 NARRATOR The suicide was confirmed by a note, left in t... 34
27 NARRATOR At the same time young Sydney stood on the le... 35
28 NARRATOR The neighbors heard, as they usually did, the... 36
29 NARRATOR -- and it was not uncommon for them to threat... 37
... ... ... ... ...
1493 DIXON We gotta get his money so we can get outta her... 382
1494 WORM That idea is over now. We're not gonna do tha... 382
1495 (to Stanley) DIXON DADDY, FUCK, DADDY, DON'T GET MAD AT ME. DON'T... 382
1496 WORM I'm not mad, son, I will not be mad at you an... 382
1497 DIXON DAD. 382
1498 DIXON I - just - thought - that - I - didn't want - ... 382
1499 WORM It's ok, boy. 382
1500 MUSIC/KERMIT THE FROG "It's not that easy bein' green... Having to s... 383
1501 DONNIE My teeff...my teeef.... 385
1502 JIM KURRING YOU'RE OK...you're gonna be ok.... 385
1503 NARRATOR And there is the account of the hanging of thr... 390
1504 NARRATOR There are stories of coincidence and chance an... 391
1505 NARRATOR ...and we generally say, "Well if that was in... 392
1506 DOCTOR Are you with us? Linda? Is it Linda? 394
1507 NARRATOR Someone's so and so meet someone else's so and... 395
1508 NARRATOR And it is in the humble opinion of this narrat... 398
1509 STANLEY Dad...Dad. 399
1510 STANLEY You have to be nicer to me, Dad. 399
1511 RICK Go to bed. 399
1512 STANLEY I think that you have to be nicer to me. 399
1513 RICK Go to bed. 399
1514 NARRATOR ...and so it goes and so it goes and the book... 400
1515 MARCIE I killed him. I killed my husband. He hit my... 401
1516 DONNIE I know that I did a thtupid thing. Tho-thtupid... 402
1517 DONNIE I really do hath love to give, I juth don't kn... 402
1518 JIM KURRING ...these security systems can be a real joke. ... 403
1519 DONNIE ....ohh-thur-I-thur-thill.... 403
1520 JIM KURRING You guys make alotta money, huh? 403
1521 (beat) JIM KURRING ...alot of people think this is just a job tha... 405
1522 END. 406

1523 rows × 4 columns

In [7]:
magnolia=pd.read_csv(name+'.csv')
stopwords = getstopwords()
In [8]:
removedchars=["'S VOICE", "'S WHISPER VOICE", " GATOR"]
for s in removedchars:
    magnolia['char']=magnolia['char'].apply(lambda x: x.replace(s, ""))
i=0
scenes=dict()
for s in magnolia.iterrows():
    scenes[s[1]['scene']]=[]
for s in magnolia.iterrows():
    scenes[s[1]['scene']].append(s[1]['char'])
for s in magnolia.iterrows():
    scenes[s[1]['scene']]=list(set(scenes[s[1]['scene']]))
In [9]:
characters=[]
for s in scenes:
    for k in scenes[s]:
        characters.append(k)
characters=list(set(characters))
appearances=dict()
for s in characters:
    appearances[s]=0
for s in magnolia.iterrows():
    appearances[s[1]['char']]=appearances[s[1]['char']]+1
In [10]:
a=pd.DataFrame(appearances, index=[i for i in range(len(appearances))])
In [11]:
finalcharacters=[]
for s in pd.DataFrame(a.transpose()[0].sort_values(0, ascending=False))[0:10].iterrows():
    finalcharacters.append(s[0])
In [12]:
finalcharacters
file=open(name+"_nodes.csv", "w")
couplesappearances=dict()
for s in finalcharacters:
    file.write(";")
    file.write(s)
file.write("\n")
for s in finalcharacters:
    newlist=[]
    for f in finalcharacters:
        newlist.append(0)
        couplesappearances[f+"_"+s]=0
    j=0
    for f in finalcharacters:
        for p in scenes:
            if f in scenes[p] and s in scenes[p] and f!=s and finalcharacters.index(f)<finalcharacters.index(s): 
                long=len(magnolia[magnolia["scene"]==p])
                newlist[j]=newlist[j]+long
                couplesappearances[f+"_"+s]=couplesappearances[f+"_"+s]+long
        j=j+1
    file.write(s)
    for f in newlist:
        file.write(";")
        file.write(str(f))
    file.write("\n")
file.close()
In [13]:
a=pd.DataFrame(couplesappearances, index=[i for i in range(len(couplesappearances))])
finalcouples=[]
for s in pd.DataFrame(a.transpose()[0].sort_values(0, ascending=False))[0:4].iterrows():
    finalcouples.append(s[0])
In [14]:
file=open(name+"_finalcharacters.csv", "w")
for s in finalcharacters:
    file.write(s+"\n")
file.close()
file=open(name+"_finalcouples.csv", "w")
for s in finalcouples:
    file.write(s+"\n")
file.close()
In [15]:
importantchars=[]
for char in appearances:
    if appearances[char]>10:
        importantchars.append(char)
In [16]:
file=open(name+"_sentiment_overtime_individual.csv", "w")
file2=open(name+"_sentiment_overtime_individualminsmaxs.csv", "w")

for k in finalcharacters:
    print(k)
    dd=getdialogue(magnolia, k, k, scenes)
    dd=[str(d) for d in dd]
    polarities, subjectivities=getsentiment(dd)
    %matplotlib inline
    import matplotlib.pyplot as plt
    moveda=maverage(polarities, dd, .99)
    plt.plot(moveda)
    i=0
    for s in moveda:
        file.write(k+","+str(float(i)/len(moveda))+", "+str(s)+"\n")
        i=i+1
    plt.ylabel('polarities')
    plt.show()
    file2.write(k+"| MIN| "+dd[moveda.index(np.min(moveda))]+"\n")
    file2.write(k+"| MAX| "+dd[moveda.index(np.max(moveda))]+"\n")
    print("MIN: "+dd[moveda.index(np.min(moveda))])
    print("\n")
    print("MAX: "+dd[moveda.index(np.max(moveda))])
    
file.close()
file2.close()

file=open(name+"_sentiment_overtime_couples.csv", "w")
file2=open(name+"_sentiment_overtime_couplesminsmaxs.csv", "w")

for k in finalcouples:
    print(k)
    liston=k.split("_")
    dd=getdialogue(magnolia, liston[0], liston[1], scenes)
    dd=[str(d) for d in dd]
    polarities, subjectivities=getsentiment(dd)
    %matplotlib inline
    import matplotlib.pyplot as plt
    moveda=maverage(polarities, dd, .99)
    plt.plot(moveda)
    i=0
    for s in moveda:
        file.write(k+","+str(float(i)/len(moveda))+", "+str(s)+"\n")
        i=i+1
    plt.ylabel('polarities')
    plt.show()
    file2.write(k+"| MIN| "+dd[moveda.index(np.min(moveda))]+"\n")
    file2.write(k+"| MAX| "+dd[moveda.index(np.max(moveda))]+"\n")
    print("MIN: "+dd[moveda.index(np.min(moveda))])
    print("\n")
    print("MAX: "+dd[moveda.index(np.max(moveda))])
    
file.close()
file2.close()
JIM KURRING
MIN: You mind if I check things back here? 


MAX: YOU'RE OK...you're gonna be ok....
JIMMY
MIN: She went crazy.  She went crazy, Rose. 


MAX: Imagine you are attending a jam session of classical composers and they have  each done an arrangment of the classic  favorite, "Whispering."  Here are three  variations on the theme, as three classic  composer's might have written it -- you are to name the composer.  The First: 
CLAUDIA
MIN: I'm sorry. 


MAX: Did you ever go out with someone and just....lie....question after question, maybe you're trying to  make yourself look cool or better  than you are or whatever, or smarter  or cooler and you just -- not really lie, but maybe you just don't say everything --
FRANK
MIN: If you feel, made to feel like you need them, like -- like you can't live if you're without them or you need, what?  They're pussy?  They're love? Fuck that.  Self Sufficient, gents.  That's the truth. What you are -- we are -- you need them  for what?  To fucking make you a piece of snot rag?  A puppett?  huh?  Hear them bitch and moan? bitch and moan --  and we're taught one thing -- go the other way -- there is No Excuse I will give you, I'm not gonna apologize -- I'm not gonna  apologize for my NEED my DESIRE...my, the  things that I need as a man to feel comfortable... You understand?  You understand?  You need to say something, "my mommy hit me or  daddy hit me or didn't let me play soccer,  so now I make mistakes, cause a that -- something, so now I piss and shit on it and do this." Bullshit.  I'm sorry. ok. yeah. no. fuck.  go.  fuck. alright. go make a new mistake. maybe not, I dunno...fuck.... 


MAX: I wouldn't want that to be misunderstood: My enrollment was totally unoffical because I was, sadly, unable to afford tuition up  there.  But there were three wonderful men who were kind enough to let me sit in on their classes, and they're names are:  Macready, Horn and Langtree among others. I was completely independent financially, and like I said: One Sad Sack A Shit.  So what we're looking at here is a true rags to riches story and I think that's  what most people respond to in "Seduce," And At The End Of The Day? Hey -- it may not  even be about picking up chicks and sticking your cock in it -- it's about finding What You Can Be In This World.  Defining It.  Controling It and  saying: I will take what is mine.  You just happen  to get a blow job out of it, then hey-what-the-fuck- why-not?  he.he.he.
PHIL
MIN: You wanna call him on the phone? We can call him, I can dial the  phone if you can remember the number -- 


MAX: Thank you, Chad, and good luck to you and your mother -- 
STANLEY
MIN: I think that you have to be nicer to me.


MAX: I'm fine. I'm fine, I just wanna keep playing --
DONNIE
MIN: My teeff...my teeef....


MAX: My name is Donnie Smith and I have lot's of love to give. 
EARL
MIN: No, no, the grade...the grade that you're in? 


MAX: "...it's not going to stop 'till you wise up..."
LINDA
MIN: listen...listen to me now, Phil:  I'm sorry, sorry I slapped your face.  ...because I don't know what I'm doing... ...I don't know how to do this, y'know?  You understand?  y'know?  I...I'm...I do things  and I fuck up and I fucked up....forgive me, ok? Can you just...


MAX: I'm listening.  I'm getting better. 
NARRATOR
MIN: -- added to this, Mr. Hansen's tortured life met before with Delmer Darion just two nights previous --


MAX: So Fay Barringer was charged with the  murder of her son and Sydney Barringer  noted as an accomplice in his own death...
JIM KURRING_CLAUDIA
MIN: You mind if I check things back here? 


MAX: ok. 
JIMMY_STANLEY
MIN: I don't mean to cry, I'm sorry. 


MAX: Imagine you are attending a jam session of classical composers and they have  each done an arrangment of the classic  favorite, "Whispering."  Here are three  variations on the theme, as three classic  composer's might have written it -- you are to name the composer.  The First: 
PHIL_EARL
MIN: -- it's not him. it's not him. He's the fuckin' asshole...Phil..c'mere... 


MAX: ...ah...maybe...yeah...she's a good one... 
FRANK_PHIL
MIN: When they put me on hold, to  talk to you...they play the tapes.  I mean: I'd seen the commercials and heard about you, but I'd never heard the tapes ....


MAX: I just...he was...but I gave him,  I just had to give him a small dose of  liquid morphine.  He hasn't been able to swallow the morphine pills so we now,  I just had to go to the liquid morphine... For the pain, you understand? 
In [17]:
for key, val in scenes.items():
    for s in scenes[key]:
        new="INSCENE_"+scenes[key][0]
        scenes[key].remove(scenes[key][0])
        scenes[key].append(new)
In [18]:
magnolia.dropna(subset=['dialogue'])
1
Out[18]:
1
In [19]:
baskets=[]
spchars=["\"", "'", ".", ",", "-"]
attributes=["?", "!"]
for s in magnolia.iterrows():
    if type(s[1]['dialogue'])!=float and  len(s[1]['dialogue'])>0:
        new=[]
        for k in scenes[s[1]['scene']]:
            new.append(k)
        new.append("SPEAKING_"+s[1]['char'])
        for k in s[1]['dialogue'].split(" "):
            ko=k
            for t in spchars:
                ko=ko.replace(t, "")
            for t in attributes:
                if ko.find(t)>=0:
                    new.append(t)
                    ko=ko.replace(t, "")
            if len(ko)>0:
                new.append(ko.lower())
        new=list(set(new))
        baskets.append(new)
In [20]:
baskets2=[]
basketslist=[]
for k in baskets:
    new=dict()
    new2=[]
    for t in k:
        if t not in stopwords:
            new[t]=1
            new2.append(t)
    baskets2.append(new)
    basketslist.append(new2)
In [21]:
baskets2=pd.DataFrame(baskets2)
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
baskets2=baskets2.fillna(0)
baskets2.to_csv(name+'_basket.csv')
In [22]:
frequent_itemsets = apriori(baskets2, min_support=5/len(baskets2), use_colnames=True)
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)
In [23]:
rules['one_lower']=[int(alllower(i) or alllower(j)) for i, j in zip(rules['antecedants'], rules['consequents'])]
In [24]:
rules['both_lower']=[int(alllower(i) and alllower(j)) for i, j in zip(rules['antecedants'], rules['consequents'])]
In [25]:
rules.to_csv(name+'_rules.csv', index=None)

Analisis de Sentimiento (Pelicula & Personaje)

Score por Pelicula

Titulo
.
ANATOLY
Numero de Palabras/Tokens en el texto original
Palabras Distintas
2036
Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.169173 11.1%
Porcentaje de Palabras encontradas por tipo de sentimiento (bing) 13.8%
sentiment Porcentaje
positive 56.2%
negative 43.8%
Porcentaje de Palabras encontradas por tipo de sentimiento (nrc) 20.8%
sentiment Porcentaje
positive 20.7%
negative 15.0%
anticipation 12.3%
trust 12.3%
joy 10.5%
sadness 6.8%
fear 6.6%
surprise 5.5%
anger 5.3%
disgust 5.0%
Porcentaje de Palabras encontradas por tipo de sentimiento (loughran) 5.45%
sentiment Porcentaje
negative 37.8%
positive 30.1%
uncertainty 25.7%
litigious 4.7%
constraining 1.7%

Score por Personaje

[1] “Analisis de Sentimientos del Personaje: ROSE” [1] “Numero total de Palabras Unicas en el texto: 696”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.181319 13.1%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 13.1%
sentiment Porcentaje
positive 52.98%
negative 47.02%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 19.3%
sentiment Porcentaje
positive 19.9%
negative 17.2%
trust 12.1%
anticipation 11.6%
joy 10.1%
sadness 8.3%
fear 6.7%
anger 4.9%
surprise 4.7%
disgust 4.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.6%
sentiment Porcentaje
negative 44.1%
uncertainty 32.2%
positive 18.6%
litigious 3.4%
constraining 1.7%

[1] “Analisis de Sentimientos del Personaje: JACK” [1] “Numero total de Palabras Unicas en el texto: 643”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.323741 10.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 12.6%
sentiment Porcentaje
positive 61.7%
negative 38.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 17%
sentiment Porcentaje
positive 17.3%
negative 15.1%
anticipation 12.0%
trust 12.0%
joy 10.9%
fear 8.7%
anger 7.3%
sadness 6.7%
disgust 5.0%
surprise 5.0%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.04%
sentiment Porcentaje
positive 47.6%
negative 26.2%
uncertainty 23.8%
litigious 2.4%

[1] “Analisis de Sentimientos del Personaje: CAL” [1] “Numero total de Palabras Unicas en el texto: 424”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.282051 13%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 14.6%
sentiment Porcentaje
positive 56.4%
negative 43.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 21.5%
sentiment Porcentaje
positive 21.7%
negative 14.0%
joy 12.7%
trust 12.3%
anticipation 12.0%
disgust 7.0%
anger 5.3%
sadness 5.3%
surprise 5.0%
fear 4.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 6.37%
sentiment Porcentaje
positive 37.2%
uncertainty 34.9%
negative 20.9%
litigious 4.7%
constraining 2.3%

[1] “Analisis de Sentimientos del Personaje: LOVETT” [1] “Numero total de Palabras Unicas en el texto: 372”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.555556 8.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 8.06%
sentiment Porcentaje
positive 66%
negative 34%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 14.8%
sentiment Porcentaje
positive 26.3%
joy 17.0%
anticipation 14.6%
trust 13.5%
negative 9.4%
sadness 6.4%
fear 5.3%
anger 2.9%
surprise 2.9%
disgust 1.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.03%
sentiment Porcentaje
positive 37.5%
negative 31.2%
uncertainty 18.8%
litigious 12.5%

[1] “Analisis de Sentimientos del Personaje: RUTH” [1] “Numero total de Palabras Unicas en el texto: 214”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.5 7.48%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.81%
sentiment Porcentaje
negative 50%
positive 50%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 13.1%
sentiment Porcentaje
positive 16.7%
negative 15.4%
anticipation 14.1%
trust 12.8%
joy 9.0%
fear 7.7%
sadness 7.7%
disgust 6.4%
surprise 6.4%
anger 3.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.61%
sentiment Porcentaje
positive 35.7%
negative 28.6%
uncertainty 21.4%
constraining 7.1%
litigious 7.1%

[1] “Analisis de Sentimientos del Personaje: BODINE” [1] “Numero total de Palabras Unicas en el texto: 288”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.466667 7.29%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 8.33%
sentiment Porcentaje
negative 53.66%
positive 46.34%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 9.03%
sentiment Porcentaje
negative 26.7%
positive 18.7%
anticipation 8.0%
disgust 8.0%
sadness 8.0%
trust 8.0%
fear 6.7%
joy 6.7%
anger 5.3%
surprise 4.0%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.12%
sentiment Porcentaje
negative 36.4%
uncertainty 36.4%
positive 18.2%
constraining 9.1%

[1] “Analisis de Sentimientos del Personaje: MOLLY” [1] “Numero total de Palabras Unicas en el texto: 228”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.647059 5.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 6.14%
sentiment Porcentaje
positive 71.4%
negative 28.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 8.33%
sentiment Porcentaje
positive 20.8%
negative 15.1%
trust 13.2%
anger 11.3%
anticipation 11.3%
surprise 9.4%
disgust 7.5%
joy 7.5%
fear 1.9%
sadness 1.9%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 2.19%
sentiment Porcentaje
negative 40%
positive 40%
constraining 20%

[1] “Analisis de Sentimientos del Personaje: SMITH” [1] “Numero total de Palabras Unicas en el texto: 140”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.933333 10%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.29%
sentiment Porcentaje
positive 60%
negative 40%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 10.7%
sentiment Porcentaje
anticipation 22.5%
positive 17.5%
negative 12.5%
trust 12.5%
fear 10.0%
joy 7.5%
sadness 7.5%
surprise 5.0%
anger 2.5%
disgust 2.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.29%
sentiment Porcentaje
negative 42.9%
uncertainty 42.9%
positive 14.3%

[1] “Analisis de Sentimientos del Personaje: ANDREWS” [1] “Numero total de Palabras Unicas en el texto: 227”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.916667 9.25%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.25%
sentiment Porcentaje
positive 70.8%
negative 29.2%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 12.8%
sentiment Porcentaje
positive 23.1%
anticipation 17.9%
trust 15.4%
joy 11.5%
negative 10.3%
surprise 9.0%
fear 3.8%
sadness 3.8%
anger 2.6%
disgust 2.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.85%
sentiment Porcentaje
negative 46.2%
positive 38.5%
litigious 7.7%
uncertainty 7.7%

[1] “Analisis de Sentimientos del Personaje: LOVEJOY” [1] “Numero total de Palabras Unicas en el texto: 148”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.625 4.73%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 5.41%
sentiment Porcentaje
positive 55.6%
negative 44.4%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 9.46%
sentiment Porcentaje
positive 35.7%
joy 17.9%
anticipation 14.3%
trust 14.3%
surprise 7.1%
anger 3.6%
fear 3.6%
negative 3.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.41%
sentiment Porcentaje
uncertainty 66.7%
negative 22.2%
positive 11.1%

[1] “Analisis de Sentimientos del Personaje: LIGHTOLLER” [1] “Numero total de Palabras Unicas en el texto: 77”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.555556 7.79%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 7.79%
sentiment Porcentaje
positive 66.7%
negative 33.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 10.4%
sentiment Porcentaje
positive 25.0%
trust 21.4%
negative 14.3%
sadness 14.3%
anticipation 10.7%
disgust 3.6%
fear 3.6%
joy 3.6%
surprise 3.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 2.6%
sentiment Porcentaje
negative 50%
positive 50%

[1] “Analisis de Sentimientos del Personaje: FABRIZIO” [1] “Numero total de Palabras Unicas en el texto: 87”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.5 8.05%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.2%
sentiment Porcentaje
positive 75%
negative 25%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 9.2%
sentiment Porcentaje
positive 20%
negative 16%
surprise 16%
joy 12%
anger 8%
anticipation 8%
trust 8%
disgust 4%
fear 4%
sadness 4%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 1.15%
sentiment Porcentaje
negative 100%

[1] “Analisis de Sentimientos del Personaje: ISMAY” [1] “Numero total de Palabras Unicas en el texto: 136”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.769231 8.82%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.56%
sentiment Porcentaje
positive 69.2%
negative 30.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 11%
sentiment Porcentaje
positive 18.6%
anticipation 16.3%
negative 14.0%
sadness 11.6%
surprise 11.6%
joy 9.3%
fear 7.0%
disgust 4.7%
trust 4.7%
anger 2.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.15%
sentiment Porcentaje
negative 42.9%
positive 42.9%
uncertainty 14.3%

[1] “Analisis de Sentimientos del Personaje: LIZZY” [1] “Numero total de Palabras Unicas en el texto: 93”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.4 5.38%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 4.3%
sentiment Porcentaje
negative 50%
positive 50%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 7.53%
sentiment Porcentaje
positive 30.8%
anticipation 23.1%
negative 15.4%
trust 15.4%
fear 7.7%
joy 7.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 1.08%
sentiment Porcentaje
uncertainty 100%

[1] “Analisis de Sentimientos del Personaje: MURDOCH” [1] “Numero total de Palabras Unicas en el texto: 73”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.857143 6.85%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 5.48%
sentiment Porcentaje
negative 100%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 9.59%
sentiment Porcentaje
negative 26.7%
anger 20.0%
positive 20.0%
trust 13.3%
anticipation 6.7%
disgust 6.7%
fear 6.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 1.37%
sentiment Porcentaje
negative 100%

[1] “Analisis de Sentimientos del Personaje: STEWARD” [1] “Numero total de Palabras Unicas en el texto: 84”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.9375 15.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 14.3%
sentiment Porcentaje
negative 71.4%
positive 28.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 15.5%
sentiment Porcentaje
positive 24.3%
anticipation 16.2%
trust 16.2%
joy 10.8%
negative 10.8%
fear 8.1%
sadness 5.4%
anger 2.7%
disgust 2.7%
surprise 2.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 7.14%
sentiment Porcentaje
negative 75.0%
positive 12.5%
uncertainty 12.5%

[1] “Analisis de Sentimientos del Personaje: TOMMY” [1] “Numero total de Palabras Unicas en el texto: 102”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.421053 14.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 11.8%
sentiment Porcentaje
positive 53.85%
negative 46.15%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 13.7%
sentiment Porcentaje
negative 25.0%
positive 12.5%
sadness 12.5%
fear 10.0%
joy 10.0%
disgust 7.5%
surprise 7.5%
anger 5.0%
anticipation 5.0%
trust 5.0%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 2.94%
sentiment Porcentaje
negative 33.3%
positive 33.3%
uncertainty 33.3%

Score por Personaje en el tiempo

Top 10 Personajes

Dialogos cúspide por Top 10 Personajes: Titanic
Personaje Min_Max Dialogo
ROSE MIN Jack, this is impossible. I can’t see you.
ROSE MAX Titanic was called the Ship of Dreams. And it was. It really was…
JACK MIN I just seem to spew ’em out. Besides, they’re not worth a damn anyway.
JACK MAX It brought me to you. And I’m thankful, Rose. I’m thankful.
CAL MIN God damn it to hell! Come on.
CAL MAX Probably best. It’ll be all business and politics, that sort of thing. Wouldn’t interest you. Good of you to come.
LOVETT MIN Would you like anything?
LOVETT MAX Just let me hold it in my hand, Rose. Please. Just once.
RUTH MIN Will the lifeboats be seated according to class? I hope they’re not too crowded –
RUTH MAX Rose, this is not a game! Our situation is precarious. You know the money’s gone!
MOLLY MIN Come on, you heard the man. Get in the boat, sister.
MOLLY MAX Well, Jack, it sounds like you’re a good man to have around in a sticky spot –
BODINE MIN Ooohh daddy-oh, are you seein’ what I’m seein’?
BODINE MAX We’ve put together the world’s largest database on the Titanic. Okay, here…
SMITH MIN Can you shore up?
SMITH MAX No, but we’re making excellent time.
ANDREWS MIN Anyone in here?
ANDREWS MAX Sleep soundly, young Rose. I have built you a good ship, strong and true. She’s all the lifeboat you need.
LOVEJOY MIN I’ve found her. She’s just over on the port side. With him.
LOVEJOY MAX Miss Rose? Hello?

Top 4 Parejas

Dialogos cúspide por Top 4 Parejas: Titanic
Parejas Min_Max Dialogo
ROSE_JACK MIN And these were drawn from life?
ROSE_JACK MAX Right.
ROSE_CAL MIN I’m not going without you.
ROSE_CAL MAX There’s the Countess Rothes. And that’s John Jacob Astor… the richest man on the ship. His little wifey there, Madeleine, is my age and in a delicate condition. See how she’s trying to hide it. Quite the scandal. And over there, that’s Sir Cosmo and Lucile, Lady Duff-Gordon. She designs naughty lingerie, among her many talents. Very popular with the royals.
ROSE_RUTH MIN You unimaginable bastard.
ROSE_RUTH MAX I don’t understand you. It is a fine match with Hockley, and it will insure our survival.
CAL_RUTH MIN Will the lifeboats be seated according to class? I hope they’re not too crowded –
CAL_RUTH MAX A real man makes his own luck, Archie.

Reglas de Asociación entre palabras (Market Basket)

Toda la pelicula

## [1] "Lift Promedio de las Reglas de Asociacion: 31.097354302274"
## [1] "Desviación estandar del Lift de las Reglas de Asociacion: 14.3179926328554"
## [1] "Deciles del Lift : "
##        10%        20%        30%        40%        50%        60% 
##   9.075472  14.800000  21.863636  29.875776  41.826087  41.826087 
##        70%        80%        90%       100% 
##  41.826087  41.826087  41.826087 192.400000

Datos del Histograma: Lift Pelicula: ANATOLY
Numero de Dialogos Lift Minimo Lift Maximo
50,670 -3 3
101,482 3 10
138,260 10 16
112,646 16 23
112,166 23 30
52,868 30 36
## [1] "Leverage Promedio de las Reglas de Asociacion: 0.00799696733196904"
## [1] "Desviación estandar del Leverage de las Reglas de Asociacion: 0.0060371080556565"
## [1] "Deciles del Leverage : "
##         10%         20%         30%         40%         50%         60% 
## 0.004846322 0.005003004 0.005073241 0.005073241 0.005073241 0.006605478 
##         70%         80%         90%        100% 
## 0.007102537 0.007102537 0.022740436 0.105869183

Datos del Histograma: Leverage pelicula: ANATOLY
Numero de Dialogos Leverage Minimo Leverage Maximo
10,778 -0.0018 0.0018
730,768 0.0018 0.0055
348,438 0.0055 0.0091
17,900 0.0091 0.013
15,232 0.013 0.016
7,210 0.016 0.02

Top 10 Personajes

Top 4 Parejas

Analisis de Relaciones entre Personajes (Pagerank)

Pagerank: Magnolia.

Pagerank: Magnolia.